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Title: INFOODS guidelines for food matching

Author
item STADLMAYR, BARBARA - Food And Agriculture Organization Of The United Nations (FAO)
item WIJESINHA-BETTONI, RAMANI - Food And Agriculture Organization Of The United Nations (FAO)
item Haytowitz, David
item RITTENSCHOBER, DORIS - Food And Agriculture Organization Of The United Nations (FAO)
item CUNNINGHAM, JUDY - Food Standards Agency
item SOBOLEWSKI, RENEE - Food Standards Agency
item EISENWAGEN, SANDRA - Food And Agriculture Organization Of The United Nations (FAO)
item BAINES, JANIS - Department Of Health And Aging
item PROBST, YASMINE - University Of Wollongong
item FITT, EMILY - Mrc Human Nutrition Research
item CHARRONDIERE, RUTH - Food And Agriculture Organization Of The United Nations (FAO)

Submitted to: Food and Agriculture Organization of the United Nations Technical Workshop Report
Publication Type: Research Technical Update
Publication Acceptance Date: 7/1/2011
Publication Date: 7/1/2011
Citation: Stadlmayr, B., Wijesinha-Bettoni, R., Haytowitz, D.B., Rittenschober, D., Cunningham, J., Sobolewski, R., Eisenwagen, S., Baines, J., Probst, Y., Fitt, E., Charrondiere, R. 2011. INFOODS guidelines for food matching. Food and Agriculture Organization of the United Nations Technical Workshop Report. www.fao.org/infoods/INFOODSGuidelinesforFoodMatchingfinal.pdf.

Interpretive Summary: It is necessary to match food consumption data with food composition data in order to calculate estimates of nutrient intakes (for nutrition purposes) or of dietary exposure (for food safety purposes). Food matching procedures are critical to obtaining high quality estimates. INFOODS has developed these guidelines for a more harmonized approach of food matching while pointing out critical steps and information needed in order to achieve the most appropriate food matches. These guidelines are intended to assist in selecting the most appropriate foods from food composition tables/databases (FCT/FCDB) and other sources when matching foods reported in food consumption surveys (at individual, household and national level) or food supply (e.g. FAOSTAT) data. Procedures for identifying the highest quality food match using a stepwise approach are described. Considerations such as processing and preparation of the food, biodiversity, color, maturity, domestication, part/source of food, refuse/edible portion, and fortification/enrichment must be identified. Selected examples of potential problems are included and possible solutions for food matching provided. These guidelines will assist users in making the best possible matches when linking food consumption data with food composition data, leading to higher quality estimates of nutrient intake and dietary exposure.

Technical Abstract: It is necessary to match food consumption data with food composition data in order to calculate estimates of nutrient intakes and dietary exposure. This can be done manually or through an automated system. As food matching procedures are key to obtaining high quality estimations of nutrient intakes (for nutrition purposes) or of dietary exposure (for food safety purposes), INFOODS has developed these guidelines to harmonize the approach of food matching while pointing out critical steps and information needed in order to achieve the most appropriate food matches. These guidelines are intended to assist in selecting the most appropriate foods in food composition tables/databases (FCT/FCDB) and other sources to match foods reported in food consumption surveys (at individual, household, and national level) or for food supply data (e.g. FAOSTAT). Procedures to identify the highest quality food match using a stepwise approach are described. Considerations such as processing and preparation of the food, biodiversity, color, maturity, domestication, part/source of food, refuse/edible portion, and fortification/enrichment are needed to determine the nutrient values of food. Selected examples of potential problems are included and possible solutions for food matching provided. These guidelines will assist users in making the best possible matches when linking food consumption data with food composition data, leading to higher quality estimates of nutrient intake and dietary exposure.